code: def test_model_construction(version):
      hp = hp_module.HyperParameters()
      hp.Choice("version", [version])
      hypermodel = efficientnet.HyperEfficientNet(
          input_shape=INPUT_SHAPE_32, classes=10
      )
      model = hypermodel.build(hp)
      assert hp.values["version"] == version
      assert model.layers
      assert model.name == "EfficientNet"
      assert model.output_shape == (None, 10)
      model.train_on_batch(
          np.ones((1,) + INPUT_SHAPE_32), np.ones((1, 10))
      )
      out = model.predict(np.ones((1,) + INPUT_SHAPE_32))
      assert out.shape == (1, 10)
  
file_path: keras-tuner/keras_tuner/applications/efficientnet_test.py
imports: import numpy
  import pytest
  from keras_tuner.applications import efficientnet
  from keras_tuner.backend import config
  from keras_tuner.backend import keras
  from keras_tuner.engine import hypermodel
  from keras_tuner.engine import hyperparameters
label_desc: Use image node_id 1 for calling a global function with example usage: test_model_construction(version) without return types